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handling-outlier

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Muhammad-Sheraz-ds / 100Days-of-Machine-Learning

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Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

reinforcement-learning exploratory-data-analysis models machine-learning-algorithms regression data-acquisition data-visualization feature-extraction supervised-learning classification feature-engineering unsupervised-learning evaluation-metrics clustering-algorithm time-series-analysis handling-missing-value handling-outlier business-problem data-preprocessing-and-cleaning
  • Updated Jun 28, 2024
  • Jupyter Notebook

PoojaP-atil / Titanic-case-study

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* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering *Accuracy score *Confusion matrix *Classification report

heatmap eda confusion-matrix scaling feature-engineering data-normalization classification-report standarization accuracy-score handling-missing-value handling-outlier handling-categorical-values handling-skewness
  • Updated Jun 4, 2024
  • Jupyter Notebook

Adyy7902 / Restaurant-Success-Prediction

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👩🏻‍🍳🍽️Restaurant Success Prediction using ML

machine-learning data-visualization sqlite3 outlier-detection restaurant feature-engineering feature-importance random-forest-classifier feature-encoding distplot hypertuning one-hot-encoding wordcloud-visualization grid-search-cross-validation success-prediction handling-outlier histplot data-cleaning-and-preprocessing model-building-and-evaluation randomised-search-cross-validation
  • Updated Sep 21, 2024
  • Jupyter Notebook
Diabetes-Predection-using-Ensemble-Learning

sumaaan / Diabetes-Predection-using-Ensemble-Learning

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This research work summarized different machine learning algorithms to create models for predicting diabetes patients utilizing the Diabetes Dataset (PIDD) from the UCI repository. The classifiers were K-Nearest Neighbors, Naïve Bayes, Support Vector, Decision Tree, Random Forest, Logistic Regression and Ensemble Model using a voting classifier.

machine-learning random-forest svm naive-bayes-classifier ensemble-learning logistic-regression confusion-matrix decision-trees feature-engineering knn boxplot datavisualization voting-classifier roc-score streamlit-webapp confusion-matrix-heatmap auc-score handling-outlier
  • Updated Jan 8, 2023
  • Jupyter Notebook

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